A Normalised Difference Vegetation Index Model for Maize Crop Performance Monitoring and Cropland Area Mapping in Sudan Ecological Zone of Nigeria

Onyibe, J. E.

National Agricultural Extension and Research Liaison Services, Ahmadu Bello University, Zaria, Nigeria.

Wahab, A. A.

National Agricultural Extension and Research Liaison Services, Ahmadu Bello University, Zaria, Nigeria.

Dahiru B.

National Agricultural Extension and Research Liaison Services, Ahmadu Bello University, Zaria, Nigeria.

Durojaiye, L. O.

National Agricultural Extension and Research Liaison Services, Ahmadu Bello University, Zaria, Nigeria.

Muibi, K. H. *

National Space Research and Development Agency, COPINE Centre, OAU, Ile-Ife, Nigeria.

*Author to whom correspondence should be addressed.


Abstract

The monitoring and mapping of crops remotely are critical for easy identification of stressed crop, prompt response to part of the crop field that requires immediate attention and the potential harvest as well as for agricultural field management. Optical remote sensing offers one of the most attractive options for vegetation indices evaluation and some optical remote sensing data are readily available free for this application, especially, Sentinel-2A, which is equipped with a multispectral sensor (MSI), which enables calculation of some vegetation indices and assessment of vegetation health and status. However, serious attention has not been given to the potential of vegetation indices calculated from MSI data in the developing countries, Nigeria inclusive. Thus, the study therefore calculated the time series NDVI for the length of the growing season for the selected crops (Maize) and geometrically calculated area of the farm plot size. In this study. The study used the Normalized Difference Vegetation Index and Supervised Image classification technique for the crop health assessment and cropland area mapping for maize. The result showed the mean, standard deviation, range, minimum and maximum NDVI values for all the farm plots over the growing season from planting period to the harvesting period for the selected crop. The average NDVI value in May which marks the onset of the growing season for maize in the study area ranges from 0.044 to 0.148. In July, which represents the period of the grain filing stage ranges from 0.136 to 0.348 and in August, which is the maturity stage for harvest ranges from 0.110 to 0.450. Also, it was observed that cropland area is 194.973269 Square Km. It is therefore evident that the results of our NDVI analysis and cropland area mapping are good insights into solving national agricultural planning problems and agricultural resources allocation for effective agricultural practices for national food security. Our results showed that vegetation indices had the greatest contributions in identifying specific crop types and crop conditions during the growing season.

Keywords: Crop performance, NDVI, Sentinel-2, cropland area


How to Cite

Onyibe, J. E., Wahab, A. A., Dahiru B., Durojaiye, L. O., & Muibi, K. H. (2024). A Normalised Difference Vegetation Index Model for Maize Crop Performance Monitoring and Cropland Area Mapping in Sudan Ecological Zone of Nigeria. Asian Journal of Advanced Research and Reports, 18(6), 10–20. https://doi.org/10.9734/ajarr/2024/v18i6649

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References

Mkhabela MS, Bullock P, Raj S, Wang S, Yang Y. Crop yield forecasting on the canadian prairies using MODIS NDVI data. Agricultural and Forest Meteorology. 2011;151:385–393.

Holden CE, Woodcock CE, An analysis of Landsat 7 and Landsat 8 underflight data and the implications for time series investigations, Remote Sensing of Environment. 2016;185:16-36 DOI: 10.1016/j.rse.2016.02.052.

Esquerdo J, Zullo J, Antunes JFG. Use of NDVI/AVHRR time-series profiles for soybean crop monitoring in Brazil. International Journal of Remote Sensing. 2011;32:3711–3727.

Crippen RE, Calculating the vegetation index faster, Remote Sensing of Environment. 1990;34: 71-73 DOI: 10.1016/0034-4257(90)90085-Z.

Haboudane D, Miller JR, Pattey E, Zarco-Tejada PJ, Strachan IB, Hyperspectral vegetation indices and novel algorithms for predicting green LAI of crop canopies: Modeling and validation in the context of precision agriculture, Remote Sensing of Environment. 2004;90:337-352 DOI: 10.1016/j.rse.2003.12.013.

Wang F-M, Huang J-F, Tang Y-L, Wang X-Z, New vegetation index and Its application in estimating leaf area index of rice, Rice Science. 2007;14:195-203 DOI: 10.1016/S1672- 6308(07)60027-4

Immitzer M, Vuolo F, Atzberger C, First experience with sentinel-2 data for crop and tree species classifications in central Europe, Remote Sensing. 2016; 8:27 DOI: 10.3390/rs8030166.

Wang X, Mochizuki K, Yamaya Y, Tani H, Kobayashi N, Sonobe R. Crop classification from Sentinel-2-derived vegetation indices using ensemble learning. J. Appl. Remote Sens. 2018;12: 026019.

Schut AGT, Stephens DJ, Stovold RGH, Adams M, Craig RL. Improved wheat yield and production forecasting with a moisture stress index, AVHRR and MODIS data. Crop & Pasture Science. 2009;60: 60–70.

Tucker CJ. Red and photographic infrared linear combinations for monitoring vegetation. Remote Sensing of Environment. 1979;8:127–150. 65.

Tilman D, Cassman KG, Matson PA, Naylor R, Polasky S. Agricultural sustainability and intensive production practices. Nature. 2002;418:671–677.

Tucker CJ. Monitoring corn and soybean crop development with hand-held radiometer spectral data, Remote Sensing of Environment. 1979;8:237- 248 DOI: 10.1016/0034-4257(79)90004-X